Instructions to use fiel1986/andromeda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use fiel1986/andromeda with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="fiel1986/andromeda", filename="qwen2.5-3b-q4_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use fiel1986/andromeda with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf fiel1986/andromeda:Q4_0 # Run inference directly in the terminal: llama cli -hf fiel1986/andromeda:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fiel1986/andromeda:Q4_0 # Run inference directly in the terminal: llama cli -hf fiel1986/andromeda:Q4_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf fiel1986/andromeda:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf fiel1986/andromeda:Q4_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf fiel1986/andromeda:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf fiel1986/andromeda:Q4_0
Use Docker
docker model run hf.co/fiel1986/andromeda:Q4_0
- LM Studio
- Jan
- Ollama
How to use fiel1986/andromeda with Ollama:
ollama run hf.co/fiel1986/andromeda:Q4_0
- Unsloth Studio
How to use fiel1986/andromeda with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fiel1986/andromeda to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fiel1986/andromeda to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fiel1986/andromeda to start chatting
- Pi
How to use fiel1986/andromeda with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fiel1986/andromeda:Q4_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "fiel1986/andromeda:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use fiel1986/andromeda with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fiel1986/andromeda:Q4_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default fiel1986/andromeda:Q4_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use fiel1986/andromeda with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fiel1986/andromeda:Q4_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "fiel1986/andromeda:Q4_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use fiel1986/andromeda with Docker Model Runner:
docker model run hf.co/fiel1986/andromeda:Q4_0
- Lemonade
How to use fiel1986/andromeda with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fiel1986/andromeda:Q4_0
Run and chat with the model
lemonade run user.andromeda-Q4_0
List all available models
lemonade list
Qwen2.5-3B (Quantized 4-bit - Q4_0)
Este repositorio contiene el modelo Qwen2.5-3B cuantizado en formato GGUF con el esquema Q4_0.
Este modelo es ideal para ejecutar inferencia localmente con baja latencia y un uso eficiente de la memoria RAM/VRAM, gracias a herramientas como llama.cpp, LM Studio, KoboldCPP o Ollama.
Detalles del Modelo
- Modelo Base: Qwen2.5-3B (de Alibaba Cloud)
- Formato: GGUF
- Nivel de Cuantizaci贸n: Q4_0 (4-bit)
- Tama帽o de archivo: Aprox. 1.8 GB - 2.0 GB (dependiendo del tokenizer)
- Capacidades: Generaci贸n de texto, chat, razonamiento b谩sico y seguimiento de instrucciones.
- Licencia: Apache 2.0 (verifique la licencia espec铆fica del modelo base en el repositorio oficial de Qwen).
C贸mo usar este modelo
Puedes cargar y ejecutar este modelo utilizando varias herramientas compatibles con GGUF.
1. Usando llama.cpp
Si tienes compilado llama.cpp, puedes ejecutar la inferencia directamente:
./main -m qwen2.5-3b-q4_0.gguf -p "Hola, 驴c贸mo est谩s?" -n 256
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